Contents

Hide code cell source
import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import df2img


sys.path.append("../../../../indicators_setup")
from ind_setup.colors import get_df_col, plotting_style
from ind_setup.tables import plot_df_table
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI, add_oni_cat

plotting_style()
from ind_setup.core import fontsize

sys.path.append("../../../functions")
from data_downloaders import GHCN, download_oni_index
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 15
     11 import df2img
     14 sys.path.append("../../../../indicators_setup")
---> 15 from ind_setup.colors import get_df_col, plotting_style
     16 from ind_setup.tables import plot_df_table
     17 from ind_setup.plotting_int import plot_oni_index_th

ModuleNotFoundError: No module named 'ind_setup'
country = 'Palau'
vars_interest = ['PRCP']
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
Hide code cell source
if update_data:
    df_country = GHCN.get_country_code(country)
    print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

    df_stations = GHCN.download_stations_info()
    df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
    print(f'There are {df_country_stations.shape[0]} stations in {country}')
Hide code cell source
if update_data:
    GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
    id = 'PSW00040309' # Koror Station
    dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
    data = dict_prcp[0]['data']#.dropna()
    data.to_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
else:
    data = pd.read_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))

st_data = data
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
if update_data:
    df1 = download_oni_index(p_data)
    df1.to_pickle(op.join(path_data, 'oni_index.pkl'))
else:
    df1 = pd.read_pickle(op.join(path_data, 'oni_index.pkl'))
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
df1['prcp'] = st_data_monthly['PRCP']#.rolling(window=rolling_mean).mean()
df1 = add_oni_cat(df1, lims = lims)
df2 = df1.resample('Y').mean()
fig= plot_bar_probs_ONI(df2, var='prcp', y_label = 'Mean Annual Precipitation')
../../../../_images/98c150a3884d68d1d8aaa48d594125eb5b8c6af36c20834cc0f8b5e48091b18c.png
df2['prcp_ref'] = df2.prcp - df2.loc['1961':'1990'].prcp.mean()
fig = plot_bar_probs_ONI(df2, var='prcp_ref', y_label = 'Precipitation [mm]')
fig.suptitle('Mean Annual Precipitation Anomaly over the 1961-1990 mean', fontsize = fontsize, y = 1.05)
plt.savefig(op.join(path_figs, 'F5_Rain_mean.png'), dpi=300, bbox_inches='tight')
../../../../_images/99c06a0eb0f5d2efd631e43408d418c56786253505687ed935d9d0edd5ffe886.png
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format, figsize = (400, 300))
df2img.save_dataframe(fig=fig, filename="getting_started.png")
../../../../_images/91ef13259a314a577048ca4e98cb7f3bc59a6cb93073fc87837493b42ec3b6aa.png